{"id":5931,"date":"2025-08-08T07:02:59","date_gmt":"2025-08-08T07:02:59","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/08\/08\/time-series-forecasting-made-simple-part-3-2-a-deep-dive-into-loess-based-smoothing\/"},"modified":"2025-08-08T07:02:59","modified_gmt":"2025-08-08T07:02:59","slug":"time-series-forecasting-made-simple-part-3-2-a-deep-dive-into-loess-based-smoothing","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/08\/08\/time-series-forecasting-made-simple-part-3-2-a-deep-dive-into-loess-based-smoothing\/","title":{"rendered":"Time Series Forecasting Made Simple (Part 3.2): A Deep Dive into LOESS-Based Smoothing"},"content":{"rendered":"<p>    Time Series Forecasting Made Simple (Part 3.2): A Deep Dive into LOESS-Based Smoothing<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>\n<p>Explore how STL uses LOESS smoothing to extract trend and seasonal components.<\/p>\n<p>The post <a href=\"https:\/\/towardsdatascience.com\/time-series-forecasting-made-simple-part-3-2-a-deep-dive-into-loess-based-smoothing\/\">Time Series Forecasting Made Simple (Part 3.2): A Deep Dive into LOESS-Based Smoothing<\/a> appeared first on <a href=\"https:\/\/towardsdatascience.com\/\">Towards Data Science<\/a>.<\/p>\n<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Nikhil Dasari<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/towardsdatascience.com\/time-series-forecasting-made-simple-part-3-2-a-deep-dive-into-loess-based-smoothing\/\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Time Series Forecasting Made Simple (Part 3.2): A Deep Dive into LOESS-Based Smoothing Explore how STL uses LOESS smoothing to extract trend and seasonal components. The post Time Series Forecasting Made Simple (Part 3.2): A Deep Dive into LOESS-Based Smoothing appeared first on Towards Data Science. Nikhil Dasari Go to original source<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,83,3458,70,157,3459,2338],"tags":[3460,3461,15],"class_list":["post-5931","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-data-science","category-loess","category-machine-learning","category-python","category-stl-decomposition","category-time-series","tag-loess","tag-smoothing","tag-time"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/5931"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=5931"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/5931\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=5931"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=5931"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=5931"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}